Software Quality Leader

Monthly Archives: March 2017

In the US, today is Pi day. March 14, or as we write it with numbers 3.14. Happy Pi day everyone!

However, in Europe, they tend to write out dates starting with the day, then the month. So, today is 14.3 over there. No-where near Pi. Instead, the closest to Pi day in Europe would be 22/7 (July 22nd), where 22/7 is a common approximation of Pi.

Which is more accurate?

Testing both approximations is pretty easy with Wolfram Alpha. The error in the approximation is determined by taking the absolute value of the difference between Pi and the approximation. So, the following screen shows the result, asking if the error in the US version is greater than the European version of Pi day:

Comparing the US version of Pi day (3.14) to the European version (22/7) with Wolfram Alpha

Vanity metrics are popular in marketing. These are metrics that allow you to feel good, but aren’t directly actionable, and are not related to your (true) goals. Vanity metrics are also easily manipulated. An example would be a hit counter, measuring page views, on a web site. What would really matter for a business web site would be the conversion rate (how many visitors actually purchase) or revenue per customer.

I’ve seen marketing campaigns that add a lot of page views, but actually cause a decrease in conversion rate. The advertising may find more viewers, but if the people are less interested in your product, its not really useful to drive up traffic. (and who knows if those viewers are really people and not bots) Measuring the impact of advertising by measuring revenue or number of visitors that become customers is more powerful.

An example in software testing is measuring the Average Age of bugs. You might start a campaign to reduce bug backlog or improve the velocity of fixing the bugs, and a measure might be the average age. However, what you are really looking for is a quicker response to every bug, not the average bug.

The average age of bugs chart from JIRA shows trends in the average age, over time.

This metric is often misleading in these efforts, as really old bugs can be fixed or closed and dramatically reducing the average age. In the chart above, the dramatic downward swings actually came from closing only a couple of bugs. Those bugs weren’t fixed, they were closed as obsolete. But, they were open in the backlog for several years, so closing them had a dramatic impact on the average age. Closing those, however, didn’t tell us anything about the responsiveness to current bugs.

Instead of Average age, tracking the median age. The median measure would be much less affected by really old bugs. Medians are a way to prevent outliers in having outsized impact on your metrics. Even better, a more direct measure of our goal to improve velocity might be to set a target timeframe, say 30 days – then measure the percentage of bugs that are fixed within that target.

These views will more directly measure your goal (improved velocity) and be less susceptible to manipulation.

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